Applications of photolithographic techniques : materials modeling for double-exposure lithography and development of shape-encoded biosensor arrays



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Double-exposure lithography has shown promise as potential resolu- tion enhancement technique that is attractive because it is much cheaper than double-patterning lithography and it can be deployed on existing imaging tools. However, this technology is not possible without the development of new materials with nonlinear response to exposure dose. Several materials have been proposed to implement a nonlinear response to exposure including re- versible contrast enhancement layers (rCELs), two-photon materials, interme- diate state two-photon (ISTP) materials, and optical threshold layers (OTLs). The performance of these materials in double-exposure applications was inves- tigated through computer simulation using a custom simulator. The results from the feasibility studies revealed that the ISTP and OTL types of materials showed much more promise than the rCEL and two-photon types of materi- als. Calculations show that two-photon materials will not be feasible unless achievable laser peak power in exposure tools can be signi¯cantly increased. Although rCEL materials demonstrated nonlinear behavior in double-exposure mode, only marginal image quality and process window improvements were ob- served. Using the results from the simulation work described herein, materials development work is currently ongoing to enable potential ISTP and OTL materials for manufacturing. A new biochip platform named \Mesoscale Unaddressed Functional- ized Features INdexed by Shape" (MUFFINS) was developed in the Willson Research Group at the University of Texas at Austin as a potential method to achieve a new low-cost biosensor system. The platform uses poly(ethylene glycol) hydrogels with bioprobes covalently cross-linked into the matrix for detection. Each sensor is shape-encoded with a unique pattern such that the information of the sensor is associated with the pattern and not its position. Large quantities of individual sensors can be produced separately and then self- assembled to form random arrays. Detection occurs through hybridization of the probes with °uorescently labeled targets. The key designs of the system include parallel batch fabrication using photolithography and self-assembly, in- creased information density using multiplexing, and enhanced shape-encoding with automated pattern recognition. The development of two aspects of the platform { self-assembly mechanics and pattern recognition algorithm, and a demonstration of all the key design elements using a single array are described herein.